Overview

Dataset statistics

Number of variables52
Number of observations10001
Missing cells0
Missing cells (%)0.0%
Duplicate rows174
Duplicate rows (%)1.7%
Total size in memory4.0 MiB
Average record size in memory416.0 B

Variable types

Numeric7
Categorical45

Alerts

soil_type_7 has constant value "0" Constant
soil_type_15 has constant value "0" Constant
Dataset has 174 (1.7%) duplicate rowsDuplicates
horizontal_distance_to_hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with horizontal_distance_to_hydrologyHigh correlation
wilderness_area1 is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
wilderness_area3 is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
soil_type_29 is highly correlated with wilderness_area1 and 1 other fieldsHigh correlation
elevation is highly correlated with Horizontal_Distance_To_Roadways and 4 other fieldsHigh correlation
wilderness_area4 is highly correlated with elevation and 3 other fieldsHigh correlation
soil_type_21 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_27 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
class is highly correlated with elevation and 1 other fieldsHigh correlation
wilderness_area2 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_2 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_3 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_17 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_15 is highly correlated with soil_type_21 and 43 other fieldsHigh correlation
soil_type_36 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_12 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_8 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_30 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_11 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_24 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_14 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_6 is highly correlated with wilderness_area4High correlation
soil_type_25 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_39 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_16 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_19 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_10 is highly correlated with elevation and 1 other fieldsHigh correlation
soil_type_13 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_20 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_38 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_33 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_1 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_9 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_31 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_32 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_22 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_23 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_34 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_7 is highly correlated with soil_type_21 and 43 other fieldsHigh correlation
soil_type_26 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_18 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_28 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_35 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_4 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_5 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_37 is highly correlated with soil_type_15 and 1 other fieldsHigh correlation
soil_type_40 is highly correlated with elevationHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with elevation and 2 other fieldsHigh correlation
horizontal_distance_to_hydrology has 423 (4.2%) zeros Zeros
Vertical_Distance_To_Hydrology has 683 (6.8%) zeros Zeros

Reproduction

Analysis started2023-01-07 05:18:02.110834
Analysis finished2023-01-07 05:18:26.616115
Duration24.51 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

elevation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1377
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2960.542546
Minimum1891
Maximum3814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-01-07T10:48:26.712301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1891
5-th percentile2415
Q12806
median3000
Q33162
95-th percentile3335
Maximum3814
Range1923
Interquartile range (IQR)356

Descriptive statistics

Standard deviation279.6504973
Coefficient of variation (CV)0.09445920568
Kurtosis0.725532232
Mean2960.542546
Median Absolute Deviation (MAD)174
Skewness-0.8074272871
Sum29608386
Variance78204.40061
MonotonicityNot monotonic
2023-01-07T10:48:26.836431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300138
 
0.4%
298837
 
0.4%
294235
 
0.3%
296534
 
0.3%
302131
 
0.3%
292930
 
0.3%
301129
 
0.3%
300829
 
0.3%
314929
 
0.3%
303129
 
0.3%
Other values (1367)9680
96.8%
ValueCountFrequency (%)
18911
< 0.1%
19162
< 0.1%
19262
< 0.1%
19281
< 0.1%
19321
< 0.1%
19391
< 0.1%
19482
< 0.1%
19631
< 0.1%
19671
< 0.1%
19701
< 0.1%
ValueCountFrequency (%)
38141
< 0.1%
38021
< 0.1%
37771
< 0.1%
37531
< 0.1%
36901
< 0.1%
36861
< 0.1%
36741
< 0.1%
36501
< 0.1%
36481
< 0.1%
36351
< 0.1%

aspect
Real number (ℝ≥0)

Distinct360
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.6730327
Minimum0
Maximum359
Zeros83
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-01-07T10:48:26.950929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q157
median125
Q3260
95-th percentile344
Maximum359
Range359
Interquartile range (IQR)203

Descriptive statistics

Standard deviation111.8877334
Coefficient of variation (CV)0.7233822951
Kurtosis-1.216398199
Mean154.6730327
Median Absolute Deviation (MAD)84
Skewness0.4146681274
Sum1546885
Variance12518.86488
MonotonicityNot monotonic
2023-01-07T10:48:27.053639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45135
 
1.3%
083
 
0.8%
31574
 
0.7%
9073
 
0.7%
13572
 
0.7%
2768
 
0.7%
6361
 
0.6%
3961
 
0.6%
3260
 
0.6%
4960
 
0.6%
Other values (350)9254
92.5%
ValueCountFrequency (%)
083
0.8%
130
 
0.3%
247
0.5%
340
0.4%
433
 
0.3%
533
 
0.3%
628
 
0.3%
728
 
0.3%
839
0.4%
947
0.5%
ValueCountFrequency (%)
35918
0.2%
35828
0.3%
35730
0.3%
35636
0.4%
35538
0.4%
35443
0.4%
35332
0.3%
35233
0.3%
35135
0.3%
35032
0.3%

slope
Real number (ℝ≥0)

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.12118788
Minimum0
Maximum60
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-01-07T10:48:27.161878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum60
Range60
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.474564337
Coefficient of variation (CV)0.5293155505
Kurtosis0.6774246413
Mean14.12118788
Median Absolute Deviation (MAD)5
Skewness0.7954095719
Sum141226
Variance55.86911203
MonotonicityNot monotonic
2023-01-07T10:48:27.260490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12612
 
6.1%
11596
 
6.0%
10580
 
5.8%
13576
 
5.8%
9520
 
5.2%
14514
 
5.1%
8503
 
5.0%
15498
 
5.0%
7452
 
4.5%
16422
 
4.2%
Other values (39)4728
47.3%
ValueCountFrequency (%)
012
 
0.1%
161
 
0.6%
2129
 
1.3%
3198
 
2.0%
4275
2.7%
5385
3.8%
6408
4.1%
7452
4.5%
8503
5.0%
9520
5.2%
ValueCountFrequency (%)
601
 
< 0.1%
571
 
< 0.1%
481
 
< 0.1%
464
< 0.1%
441
 
< 0.1%
435
< 0.1%
423
 
< 0.1%
414
< 0.1%
405
< 0.1%
398
0.1%

horizontal_distance_to_hydrology
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct349
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.0122988
Minimum0
Maximum1356
Zeros423
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-01-07T10:48:27.363532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median216
Q3384
95-th percentile680
Maximum1356
Range1356
Interquartile range (IQR)276

Descriptive statistics

Standard deviation211.5848812
Coefficient of variation (CV)0.7924162376
Kurtosis1.415242863
Mean267.0122988
Median Absolute Deviation (MAD)131
Skewness1.155646571
Sum2670390
Variance44768.16195
MonotonicityNot monotonic
2023-01-07T10:48:27.467564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30610
 
6.1%
0423
 
4.2%
150348
 
3.5%
60320
 
3.2%
67270
 
2.7%
85262
 
2.6%
108241
 
2.4%
90223
 
2.2%
42220
 
2.2%
120195
 
1.9%
Other values (339)6889
68.9%
ValueCountFrequency (%)
0423
4.2%
30610
6.1%
42220
 
2.2%
60320
3.2%
67270
2.7%
85262
2.6%
90223
 
2.2%
95158
 
1.6%
108241
 
2.4%
120195
 
1.9%
ValueCountFrequency (%)
13561
 
< 0.1%
12731
 
< 0.1%
12531
 
< 0.1%
12372
< 0.1%
12341
 
< 0.1%
12091
 
< 0.1%
12003
< 0.1%
11881
 
< 0.1%
11722
< 0.1%
11671
 
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct404
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.40845915
Minimum-147
Maximum463
Zeros683
Zeros (%)6.8%
Negative939
Negative (%)9.4%
Memory size78.3 KiB
2023-01-07T10:48:27.571057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-147
5-th percentile-8
Q17
median29
Q370
95-th percentile166
Maximum463
Range610
Interquartile range (IQR)63

Descriptive statistics

Standard deviation57.93035857
Coefficient of variation (CV)1.248271536
Kurtosis3.960834615
Mean46.40845915
Median Absolute Deviation (MAD)27
Skewness1.654804585
Sum464131
Variance3355.926444
MonotonicityNot monotonic
2023-01-07T10:48:27.832538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0683
 
6.8%
3164
 
1.6%
6152
 
1.5%
4149
 
1.5%
10141
 
1.4%
7138
 
1.4%
1135
 
1.3%
13133
 
1.3%
9133
 
1.3%
23133
 
1.3%
Other values (394)8040
80.4%
ValueCountFrequency (%)
-1471
< 0.1%
-1302
< 0.1%
-1251
< 0.1%
-1211
< 0.1%
-1161
< 0.1%
-1132
< 0.1%
-1121
< 0.1%
-1052
< 0.1%
-1011
< 0.1%
-971
< 0.1%
ValueCountFrequency (%)
4631
< 0.1%
4311
< 0.1%
4051
< 0.1%
3951
< 0.1%
3931
< 0.1%
3921
< 0.1%
3801
< 0.1%
3491
< 0.1%
3481
< 0.1%
3461
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3448
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2333.747025
Minimum0
Maximum6942
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-01-07T10:48:27.932513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379
Q11106
median1991
Q33282
95-th percentile5436
Maximum6942
Range6942
Interquartile range (IQR)2176

Descriptive statistics

Standard deviation1536.500439
Coefficient of variation (CV)0.6583834592
Kurtosis-0.3466448877
Mean2333.747025
Median Absolute Deviation (MAD)1018
Skewness0.7178935579
Sum23339804
Variance2360833.6
MonotonicityNot monotonic
2023-01-07T10:48:28.041776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15025
 
0.2%
102022
 
0.2%
94219
 
0.2%
96019
 
0.2%
114019
 
0.2%
99718
 
0.2%
120017
 
0.2%
75217
 
0.2%
61816
 
0.2%
99016
 
0.2%
Other values (3438)9813
98.1%
ValueCountFrequency (%)
02
 
< 0.1%
303
 
< 0.1%
421
 
< 0.1%
605
 
< 0.1%
675
 
< 0.1%
856
0.1%
905
 
< 0.1%
959
0.1%
10813
0.1%
1209
0.1%
ValueCountFrequency (%)
69421
< 0.1%
69161
< 0.1%
67851
< 0.1%
67321
< 0.1%
67211
< 0.1%
66841
< 0.1%
66771
< 0.1%
66412
< 0.1%
66161
< 0.1%
65951
< 0.1%
Distinct2889
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1969.249375
Minimum30
Maximum7110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-01-07T10:48:28.136467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile426
Q11026
median1695
Q32539
95-th percentile4932
Maximum7110
Range7080
Interquartile range (IQR)1513

Descriptive statistics

Standard deviation1313.107337
Coefficient of variation (CV)0.6668060196
Kurtosis1.725009728
Mean1969.249375
Median Absolute Deviation (MAD)735
Skewness1.316048503
Sum19694463
Variance1724250.88
MonotonicityNot monotonic
2023-01-07T10:48:28.232604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99727
 
0.3%
96022
 
0.2%
114221
 
0.2%
150020
 
0.2%
99020
 
0.2%
61820
 
0.2%
147119
 
0.2%
111019
 
0.2%
58218
 
0.2%
60718
 
0.2%
Other values (2879)9797
98.0%
ValueCountFrequency (%)
303
 
< 0.1%
423
 
< 0.1%
602
 
< 0.1%
677
0.1%
855
< 0.1%
901
 
< 0.1%
958
0.1%
1082
 
< 0.1%
1205
< 0.1%
1246
0.1%
ValueCountFrequency (%)
71101
< 0.1%
70911
< 0.1%
70061
< 0.1%
69951
< 0.1%
69621
< 0.1%
68791
< 0.1%
68331
< 0.1%
67421
< 0.1%
67321
< 0.1%
67261
< 0.1%

wilderness_area1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
5537 
1
4464 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
05537
55.4%
14464
44.6%

Length

2023-01-07T10:48:28.330503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:28.416293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
05537
55.4%
14464
44.6%

Most occurring characters

ValueCountFrequency (%)
05537
55.4%
14464
44.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05537
55.4%
14464
44.6%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05537
55.4%
14464
44.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05537
55.4%
14464
44.6%

wilderness_area2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9493 
1
 
508

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
09493
94.9%
1508
 
5.1%

Length

2023-01-07T10:48:28.480510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:28.549286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09493
94.9%
1508
 
5.1%

Most occurring characters

ValueCountFrequency (%)
09493
94.9%
1508
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09493
94.9%
1508
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09493
94.9%
1508
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09493
94.9%
1508
 
5.1%

wilderness_area3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
5599 
1
4402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05599
56.0%
14402
44.0%

Length

2023-01-07T10:48:28.598895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:28.724205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
05599
56.0%
14402
44.0%

Most occurring characters

ValueCountFrequency (%)
05599
56.0%
14402
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05599
56.0%
14402
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05599
56.0%
14402
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05599
56.0%
14402
44.0%

wilderness_area4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9374 
1
 
627

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09374
93.7%
1627
 
6.3%

Length

2023-01-07T10:48:28.805187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:28.937513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09374
93.7%
1627
 
6.3%

Most occurring characters

ValueCountFrequency (%)
09374
93.7%
1627
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09374
93.7%
1627
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09374
93.7%
1627
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09374
93.7%
1627
 
6.3%

soil_type_1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9941 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09941
99.4%
160
 
0.6%

Length

2023-01-07T10:48:29.032535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:29.158204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09941
99.4%
160
 
0.6%

Most occurring characters

ValueCountFrequency (%)
09941
99.4%
160
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09941
99.4%
160
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09941
99.4%
160
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09941
99.4%
160
 
0.6%

soil_type_2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9873 
1
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09873
98.7%
1128
 
1.3%

Length

2023-01-07T10:48:29.288325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:29.404079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09873
98.7%
1128
 
1.3%

Most occurring characters

ValueCountFrequency (%)
09873
98.7%
1128
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09873
98.7%
1128
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09873
98.7%
1128
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09873
98.7%
1128
 
1.3%

soil_type_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9933 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09933
99.3%
168
 
0.7%

Length

2023-01-07T10:48:29.499611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:29.649648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09933
99.3%
168
 
0.7%

Most occurring characters

ValueCountFrequency (%)
09933
99.3%
168
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09933
99.3%
168
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09933
99.3%
168
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09933
99.3%
168
 
0.7%

soil_type_4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9792 
1
 
209

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09792
97.9%
1209
 
2.1%

Length

2023-01-07T10:48:29.792558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:29.977653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09792
97.9%
1209
 
2.1%

Most occurring characters

ValueCountFrequency (%)
09792
97.9%
1209
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09792
97.9%
1209
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09792
97.9%
1209
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09792
97.9%
1209
 
2.1%

soil_type_5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9971 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09971
99.7%
130
 
0.3%

Length

2023-01-07T10:48:30.083681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:30.165695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09971
99.7%
130
 
0.3%

Most occurring characters

ValueCountFrequency (%)
09971
99.7%
130
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09971
99.7%
130
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09971
99.7%
130
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09971
99.7%
130
 
0.3%

soil_type_6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9906 
1
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09906
99.1%
195
 
0.9%

Length

2023-01-07T10:48:30.231107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:30.299676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09906
99.1%
195
 
0.9%

Most occurring characters

ValueCountFrequency (%)
09906
99.1%
195
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09906
99.1%
195
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09906
99.1%
195
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09906
99.1%
195
 
0.9%

soil_type_7
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
10001 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010001
100.0%

Length

2023-01-07T10:48:30.366362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:30.439053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010001
100.0%

Most occurring characters

ValueCountFrequency (%)
010001
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010001
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010001
100.0%

soil_type_8
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9999 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09999
> 99.9%
12
 
< 0.1%

Length

2023-01-07T10:48:30.497380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:30.570532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09999
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09999
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09999
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09999
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09999
> 99.9%
12
 
< 0.1%

soil_type_9
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9978 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09978
99.8%
123
 
0.2%

Length

2023-01-07T10:48:30.633560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:30.711388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09978
99.8%
123
 
0.2%

Most occurring characters

ValueCountFrequency (%)
09978
99.8%
123
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09978
99.8%
123
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09978
99.8%
123
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09978
99.8%
123
 
0.2%

soil_type_10
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9425 
1
 
576

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09425
94.2%
1576
 
5.8%

Length

2023-01-07T10:48:30.785584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:30.866608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09425
94.2%
1576
 
5.8%

Most occurring characters

ValueCountFrequency (%)
09425
94.2%
1576
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09425
94.2%
1576
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09425
94.2%
1576
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09425
94.2%
1576
 
5.8%

soil_type_11
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9784 
1
 
217

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09784
97.8%
1217
 
2.2%

Length

2023-01-07T10:48:30.936630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:31.018642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09784
97.8%
1217
 
2.2%

Most occurring characters

ValueCountFrequency (%)
09784
97.8%
1217
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09784
97.8%
1217
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09784
97.8%
1217
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09784
97.8%
1217
 
2.2%

soil_type_12
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9513 
1
 
488

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09513
95.1%
1488
 
4.9%

Length

2023-01-07T10:48:31.089969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:31.177826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09513
95.1%
1488
 
4.9%

Most occurring characters

ValueCountFrequency (%)
09513
95.1%
1488
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09513
95.1%
1488
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09513
95.1%
1488
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09513
95.1%
1488
 
4.9%

soil_type_13
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9700 
1
 
301

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09700
97.0%
1301
 
3.0%

Length

2023-01-07T10:48:31.245841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:31.329874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09700
97.0%
1301
 
3.0%

Most occurring characters

ValueCountFrequency (%)
09700
97.0%
1301
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09700
97.0%
1301
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09700
97.0%
1301
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09700
97.0%
1301
 
3.0%

soil_type_14
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9989 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09989
99.9%
112
 
0.1%

Length

2023-01-07T10:48:31.401878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:31.483897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09989
99.9%
112
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09989
99.9%
112
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09989
99.9%
112
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09989
99.9%
112
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09989
99.9%
112
 
0.1%

soil_type_15
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
10001 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010001
100.0%

Length

2023-01-07T10:48:31.552918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:31.627941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010001
100.0%

Most occurring characters

ValueCountFrequency (%)
010001
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010001
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010001
100.0%

soil_type_16
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9952 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09952
99.5%
149
 
0.5%

Length

2023-01-07T10:48:31.697142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:31.776174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09952
99.5%
149
 
0.5%

Most occurring characters

ValueCountFrequency (%)
09952
99.5%
149
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09952
99.5%
149
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09952
99.5%
149
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09952
99.5%
149
 
0.5%

soil_type_17
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9938 
1
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09938
99.4%
163
 
0.6%

Length

2023-01-07T10:48:31.841188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:31.918199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09938
99.4%
163
 
0.6%

Most occurring characters

ValueCountFrequency (%)
09938
99.4%
163
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09938
99.4%
163
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09938
99.4%
163
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09938
99.4%
163
 
0.6%

soil_type_18
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9977 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09977
99.8%
124
 
0.2%

Length

2023-01-07T10:48:31.984189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:32.067233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09977
99.8%
124
 
0.2%

Most occurring characters

ValueCountFrequency (%)
09977
99.8%
124
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09977
99.8%
124
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09977
99.8%
124
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09977
99.8%
124
 
0.2%

soil_type_19
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9950 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09950
99.5%
151
 
0.5%

Length

2023-01-07T10:48:32.143242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:32.218333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09950
99.5%
151
 
0.5%

Most occurring characters

ValueCountFrequency (%)
09950
99.5%
151
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09950
99.5%
151
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09950
99.5%
151
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09950
99.5%
151
 
0.5%

soil_type_20
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9822 
1
 
179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09822
98.2%
1179
 
1.8%

Length

2023-01-07T10:48:32.281945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:32.614879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09822
98.2%
1179
 
1.8%

Most occurring characters

ValueCountFrequency (%)
09822
98.2%
1179
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09822
98.2%
1179
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09822
98.2%
1179
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09822
98.2%
1179
 
1.8%

soil_type_21
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9988 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09988
99.9%
113
 
0.1%

Length

2023-01-07T10:48:32.664972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:32.752912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09988
99.9%
113
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09988
99.9%
113
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09988
99.9%
113
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09988
99.9%
113
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09988
99.9%
113
 
0.1%

soil_type_22
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9438 
1
 
563

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
09438
94.4%
1563
 
5.6%

Length

2023-01-07T10:48:32.808985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:32.890362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09438
94.4%
1563
 
5.6%

Most occurring characters

ValueCountFrequency (%)
09438
94.4%
1563
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09438
94.4%
1563
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09438
94.4%
1563
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09438
94.4%
1563
 
5.6%

soil_type_23
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9018 
1
983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09018
90.2%
1983
 
9.8%

Length

2023-01-07T10:48:32.957706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:33.038725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09018
90.2%
1983
 
9.8%

Most occurring characters

ValueCountFrequency (%)
09018
90.2%
1983
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09018
90.2%
1983
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09018
90.2%
1983
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09018
90.2%
1983
 
9.8%

soil_type_24
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9617 
1
 
384

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09617
96.2%
1384
 
3.8%

Length

2023-01-07T10:48:33.105087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:33.182714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09617
96.2%
1384
 
3.8%

Most occurring characters

ValueCountFrequency (%)
09617
96.2%
1384
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09617
96.2%
1384
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09617
96.2%
1384
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09617
96.2%
1384
 
3.8%

soil_type_25
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9992 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09992
99.9%
19
 
0.1%

Length

2023-01-07T10:48:33.246741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:33.323067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09992
99.9%
19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09992
99.9%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09992
99.9%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09992
99.9%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09992
99.9%
19
 
0.1%

soil_type_26
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9946 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09946
99.5%
155
 
0.5%

Length

2023-01-07T10:48:33.390115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:33.468670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09946
99.5%
155
 
0.5%

Most occurring characters

ValueCountFrequency (%)
09946
99.5%
155
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09946
99.5%
155
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09946
99.5%
155
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09946
99.5%
155
 
0.5%

soil_type_27
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9979 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09979
99.8%
122
 
0.2%

Length

2023-01-07T10:48:33.532207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:33.603583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09979
99.8%
122
 
0.2%

Most occurring characters

ValueCountFrequency (%)
09979
99.8%
122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09979
99.8%
122
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09979
99.8%
122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09979
99.8%
122
 
0.2%

soil_type_28
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9980 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Length

2023-01-07T10:48:33.665596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:33.739614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring characters

ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

soil_type_29
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7996 
1
2005 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
07996
80.0%
12005
 
20.0%

Length

2023-01-07T10:48:33.804628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:33.882647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
07996
80.0%
12005
 
20.0%

Most occurring characters

ValueCountFrequency (%)
07996
80.0%
12005
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07996
80.0%
12005
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07996
80.0%
12005
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07996
80.0%
12005
 
20.0%

soil_type_30
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9518 
1
 
483

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09518
95.2%
1483
 
4.8%

Length

2023-01-07T10:48:33.949042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:34.044406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09518
95.2%
1483
 
4.8%

Most occurring characters

ValueCountFrequency (%)
09518
95.2%
1483
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09518
95.2%
1483
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09518
95.2%
1483
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09518
95.2%
1483
 
4.8%

soil_type_31
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9572 
1
 
429

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09572
95.7%
1429
 
4.3%

Length

2023-01-07T10:48:34.097494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:34.185266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09572
95.7%
1429
 
4.3%

Most occurring characters

ValueCountFrequency (%)
09572
95.7%
1429
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09572
95.7%
1429
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09572
95.7%
1429
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09572
95.7%
1429
 
4.3%

soil_type_32
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9033 
1
968 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09033
90.3%
1968
 
9.7%

Length

2023-01-07T10:48:34.269443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:34.417306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09033
90.3%
1968
 
9.7%

Most occurring characters

ValueCountFrequency (%)
09033
90.3%
1968
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09033
90.3%
1968
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09033
90.3%
1968
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09033
90.3%
1968
 
9.7%

soil_type_33
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9239 
1
 
762

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09239
92.4%
1762
 
7.6%

Length

2023-01-07T10:48:34.519329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:34.626884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09239
92.4%
1762
 
7.6%

Most occurring characters

ValueCountFrequency (%)
09239
92.4%
1762
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09239
92.4%
1762
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09239
92.4%
1762
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09239
92.4%
1762
 
7.6%

soil_type_34
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9980 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Length

2023-01-07T10:48:34.705513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:34.934830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring characters

ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09980
99.8%
121
 
0.2%

soil_type_35
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9966 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09966
99.7%
135
 
0.3%

Length

2023-01-07T10:48:35.085138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:35.306446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09966
99.7%
135
 
0.3%

Most occurring characters

ValueCountFrequency (%)
09966
99.7%
135
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09966
99.7%
135
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09966
99.7%
135
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09966
99.7%
135
 
0.3%

soil_type_36
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9998 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09998
> 99.9%
13
 
< 0.1%

Length

2023-01-07T10:48:35.418018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:35.589571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09998
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09998
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09998
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09998
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09998
> 99.9%
13
 
< 0.1%

soil_type_37
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
10000 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010000
> 99.9%
11
 
< 0.1%

Length

2023-01-07T10:48:35.732818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:35.852154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010000
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
010000
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010000
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010000
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010000
> 99.9%
11
 
< 0.1%

soil_type_38
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9742 
1
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09742
97.4%
1259
 
2.6%

Length

2023-01-07T10:48:35.980899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:36.082058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09742
97.4%
1259
 
2.6%

Most occurring characters

ValueCountFrequency (%)
09742
97.4%
1259
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09742
97.4%
1259
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09742
97.4%
1259
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09742
97.4%
1259
 
2.6%

soil_type_39
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9771 
1
 
230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09771
97.7%
1230
 
2.3%

Length

2023-01-07T10:48:36.193114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:36.303346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09771
97.7%
1230
 
2.3%

Most occurring characters

ValueCountFrequency (%)
09771
97.7%
1230
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09771
97.7%
1230
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09771
97.7%
1230
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09771
97.7%
1230
 
2.3%

soil_type_40
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9821 
1
 
180

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09821
98.2%
1180
 
1.8%

Length

2023-01-07T10:48:36.392637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:36.502811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09821
98.2%
1180
 
1.8%

Most occurring characters

ValueCountFrequency (%)
09821
98.2%
1180
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09821
98.2%
1180
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common10001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09821
98.2%
1180
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09821
98.2%
1180
 
1.8%

class
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Lodgepole_Pine
4621 
Spruce_Fir
3563 
Ponderosa_Pine
659 
Krummholz
 
435
Douglas_fir
 
355
Other values (2)
 
368

Length

Max length17
Median length14
Mean length12.06379362
Min length5

Characters and Unicode

Total characters120650
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDouglas_fir
2nd rowLodgepole_Pine
3rd rowLodgepole_Pine
4th rowSpruce_Fir
5th rowSpruce_Fir

Common Values

ValueCountFrequency (%)
Lodgepole_Pine4621
46.2%
Spruce_Fir3563
35.6%
Ponderosa_Pine659
 
6.6%
Krummholz435
 
4.3%
Douglas_fir355
 
3.5%
Aspen248
 
2.5%
Cottonwood_Willow120
 
1.2%

Length

2023-01-07T10:48:36.631712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-07T10:48:36.758353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
lodgepole_pine4621
46.2%
spruce_fir3563
35.6%
ponderosa_pine659
 
6.6%
krummholz435
 
4.3%
douglas_fir355
 
3.5%
aspen248
 
2.5%
cottonwood_willow120
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e18992
15.7%
o11950
 
9.9%
_9318
 
7.7%
i9318
 
7.7%
r8575
 
7.1%
p8432
 
7.0%
n6307
 
5.2%
P5939
 
4.9%
l5651
 
4.7%
d5400
 
4.5%
Other values (19)30768
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter92368
76.6%
Uppercase Letter18964
 
15.7%
Connector Punctuation9318
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e18992
20.6%
o11950
12.9%
i9318
10.1%
r8575
9.3%
p8432
9.1%
n6307
 
6.8%
l5651
 
6.1%
d5400
 
5.8%
g4976
 
5.4%
u4353
 
4.7%
Other values (9)8414
9.1%
Uppercase Letter
ValueCountFrequency (%)
P5939
31.3%
L4621
24.4%
F3563
18.8%
S3563
18.8%
K435
 
2.3%
D355
 
1.9%
A248
 
1.3%
C120
 
0.6%
W120
 
0.6%
Connector Punctuation
ValueCountFrequency (%)
_9318
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111332
92.3%
Common9318
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e18992
17.1%
o11950
10.7%
i9318
 
8.4%
r8575
 
7.7%
p8432
 
7.6%
n6307
 
5.7%
P5939
 
5.3%
l5651
 
5.1%
d5400
 
4.9%
g4976
 
4.5%
Other values (18)25792
23.2%
Common
ValueCountFrequency (%)
_9318
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII120650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e18992
15.7%
o11950
 
9.9%
_9318
 
7.7%
i9318
 
7.7%
r8575
 
7.1%
p8432
 
7.0%
n6307
 
5.2%
P5939
 
4.9%
l5651
 
4.7%
d5400
 
4.5%
Other values (19)30768
25.5%

Interactions

2023-01-07T10:48:24.961164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:20.309883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.161426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.890635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.601842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.342877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.195609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:25.072050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:20.460455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.270450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.990538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.705621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.449517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.303026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:25.181725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:20.574383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.372196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.089198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.811025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.549185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.410196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:25.290951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:20.715860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.471637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.187319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.912003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.644232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.523296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:25.391811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:20.828189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.577027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.294631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.018502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.882457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.636321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:25.485371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:20.933213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.675142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.393640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.122968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.982667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.742221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:25.600973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.044115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:21.785610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:22.497461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:23.230162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.088588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-07T10:48:24.851239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-07T10:48:36.967190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-07T10:48:38.332081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-07T10:48:40.143021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-07T10:48:41.828113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-07T10:48:42.456825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-07T10:48:25.936835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

elevationaspectslopehorizontal_distance_to_hydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHorizontal_Distance_To_Fire_Pointswilderness_area1wilderness_area2wilderness_area3wilderness_area4soil_type_1soil_type_2soil_type_3soil_type_4soil_type_5soil_type_6soil_type_7soil_type_8soil_type_9soil_type_10soil_type_11soil_type_12soil_type_13soil_type_14soil_type_15soil_type_16soil_type_17soil_type_18soil_type_19soil_type_20soil_type_21soil_type_22soil_type_23soil_type_24soil_type_25soil_type_26soil_type_27soil_type_28soil_type_29soil_type_30soil_type_31soil_type_32soil_type_33soil_type_34soil_type_35soil_type_36soil_type_37soil_type_38soil_type_39soil_type_40class
024241131626868808126000010100000000000000000000000000000000000000Douglas_fir
131304015330242279165000100000000000000000000000000000000100000000Lodgepole_Pine
23071625212401321269710000000000000000000000000000000100000000000Lodgepole_Pine
331519179519577202901000000000000000000000001000000000000000000Spruce_Fir
4313974328392522146310000000000000000000000000000000100000000000Spruce_Fir
5308932871757242193601000000000000000000000001000000000000000000Spruce_Fir
632242920134261052154310000000000000000000000000100000000000000000Ponderosa_Pine
7277898109514286324000100000000001000000000000000000000000000000Lodgepole_Pine
8282234821609244092700100000000000000000000000000000001000000000Lodgepole_Pine
92768612466-1972178900100000000000100000000000000000000000000000Lodgepole_Pine

Last rows

elevationaspectslopehorizontal_distance_to_hydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHorizontal_Distance_To_Fire_Pointswilderness_area1wilderness_area2wilderness_area3wilderness_area4soil_type_1soil_type_2soil_type_3soil_type_4soil_type_5soil_type_6soil_type_7soil_type_8soil_type_9soil_type_10soil_type_11soil_type_12soil_type_13soil_type_14soil_type_15soil_type_16soil_type_17soil_type_18soil_type_19soil_type_20soil_type_21soil_type_22soil_type_23soil_type_24soil_type_25soil_type_26soil_type_27soil_type_28soil_type_29soil_type_30soil_type_31soil_type_32soil_type_33soil_type_34soil_type_35soil_type_36soil_type_37soil_type_38soil_type_39soil_type_40class
999129053919537782072130000100000000000100000000000000000000000000000Ponderosa_Pine
999226351712695561772123400100001000000000000000000000000000000000000Ponderosa_Pine
999330963182032387698174400100000000000000000000001000000000000000000Spruce_Fir
9994307611018175494846294010000000000000000000000000000000100000000000Lodgepole_Pine
9995327718818330891266271710000000000000000000000000000000100000000000Spruce_Fir
999629131731721058511104000100000000000001000000000000000000000000000Aspen
9997292834914001165228700100000000000000000000000000000000100000000Lodgepole_Pine
99983329165139972132584224200100000000000000000000000000000000010000000Spruce_Fir
9999313511120649-59582111601000000000000000000001000000000000000000000Lodgepole_Pine
1000028340123034559478810000000000000000001000000000000000000000000Lodgepole_Pine

Duplicate rows

Most frequently occurring

elevationaspectslopehorizontal_distance_to_hydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHorizontal_Distance_To_Fire_Pointswilderness_area1wilderness_area2wilderness_area3wilderness_area4soil_type_1soil_type_2soil_type_3soil_type_4soil_type_5soil_type_6soil_type_7soil_type_8soil_type_9soil_type_10soil_type_11soil_type_12soil_type_13soil_type_14soil_type_15soil_type_16soil_type_17soil_type_18soil_type_19soil_type_20soil_type_21soil_type_22soil_type_23soil_type_24soil_type_25soil_type_26soil_type_27soil_type_28soil_type_29soil_type_30soil_type_31soil_type_32soil_type_33soil_type_34soil_type_35soil_type_36soil_type_37soil_type_38soil_type_39soil_type_40class# duplicates
47285582795222796277810000000000000010000000000000000000000000000Lodgepole_Pine3
162331428716666163312278700100000000000000000000000000000000000000010Spruce_Fir3
019483920301315056600011000000000000000000000000000000000000000Ponderosa_Pine2
1209759261903969381100010000010000000000000000000000000000000000Ponderosa_Pine2
2215431321203355132400010000000001000000000000000000000000000000Douglas_fir2
32185320271709678595300010000000001000000000000000000000000000000Douglas_fir2
422153021040019657024000010000100000000000000000000000000000000000Ponderosa_Pine2
52247356344979057967100010000000001000000000000000000000000000000Ponderosa_Pine2
6233734328218137137092400010000000001000000000000000000000000000000Ponderosa_Pine2
7237451500693122400010000000000000100000000000000000000000000Douglas_fir2